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  • 1.
    Alexandre, Rui Carlos Josino
    et al.
    UNIFESP, Brazil.
    Martins, Luiz Eduardo Galvao
    UNIFESP, Brazil.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Cybersecurity Risk Assessment for Medium-Risk Drones: A Systematic Literature Review2023In: IEEE Aerospace and Electronic Systems Magazine, ISSN 0885-8985, E-ISSN 1557-959X, Vol. 38, no 6, p. 28-43Article, review/survey (Refereed)
    Abstract [en]

    The increased demand for Remotely Piloted Aircraft Systems (RPAS) in Beyond Visual Line-Of-Sight (BVLOS) operations gives rise to a set of concerns regarding cybersecurity that, if not addressed, can lead to the unsafe operation of RPASs. To assist the airworthiness evaluation that is performed by Civil Aviation Authorities (CAAs), we identified several processes that are used to evaluate the cybersecurity of RPAS. We conducted a Systematic Literature Review (SLR) by selecting 30 papers (out of 211 screened) that were published during the past five years. The results of our SLR indicate the importance of cybersecurity to the safe operation of RPAS. It is evident that there is a lack of a systematic process to enable a cybersecurity review of RPAS. We observe that common cyber threats to RPAS are related to jamming, spoofing, and DOS/DDOS (Denial of Service/Distributed Denial of Service). Processes relevant to the assessment of RPAS cybersecurity exist, however they differ in safety concerns from our perspective. In addition, with only one exception, the methods have not been used, and/or the use has not been reported as pertaining to industrial application. The most frequently cited vulnerabilities are those related to GPS and datalinks. 

  • 2.
    Ali, Nauman bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Tanveer, Binish
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Comparison of Citation Sources for Reference and Citation-Based Search in Systematic Literature Reviews2022In: e-Informatica Software Engineering Journal, ISSN 1897-7979, E-ISSN 2084-4840, Vol. 16, no 1, article id 220106Article, review/survey (Refereed)
    Abstract [en]

    Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature. EVIE © 2022 The Authors.

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  • 3.
    Alégroth, Emil
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersén, Elin
    Linköping University, SWE.
    Tinnerholm, John
    Linköping University, SWE.
    A Failed attempt at creating Guidelines for Visual GUI Testing: An industrial case study2021In: Proceedings - 2021 IEEE 14th International Conference on Software Testing, Verification and Validation, ICST 2021, Institute of Electrical and Electronics Engineers Inc. , 2021, p. 340-350, article id 9438551Conference paper (Refereed)
    Abstract [en]

    Software development is governed by guidelines that aim to improve the code's qualities, such as maintainability. However, whilst coding guidelines are commonplace for software, guidelines for testware are much less common. In particular, for GUI-based tests driven with image recognition, also referred to as Visual GUI Testing (VGT), explicit coding guidelines are missing.In this industrial case study, performed at the Swedish defence contractor Saab AB, we propose a set of coding guidelines for VGT and evaluate their impact on test scripts for an industrial, safety-critical system. To study the guidelines' effect on maintenance costs, five representative manual test cases are each translated with and without the proposed guidelines in the two VGT tools SikuliX and EyeAutomate. As such, 20 test scripts were developed, with a combined development cost of more than 100 man-hours. Three of the tests are then maintained by one researcher and two practitioners for another version of the system and costs measured to evaluate return on investment. This analysis is complemented with observations and interviews to elicit practitioners' perceptions and experiences with VGT.Results show that scripts developed with the guidelines had higher maintenance costs than scripts developed without guidelines. This is supported by qualitative results that many of the guidelines are considered inappropriate, superfluous or unnecessary due to the inherent properties of the scripts, e.g. their natural small size, linear flows, natural separation of concerns, and more. We conclude that there are differences between VGT scripts and software that prohibit direct translation of guidelines between the two. As such, we consider our study as a failure but argue that several lessons can be drawn from our results to guide future research into guidelines for VGT and GUI-based test automation. © 2021 IEEE.

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  • 4.
    Badampudi, Deepika
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Britto, Ricardo
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Modern code reviews: Preliminary results of a systematic mapping study2019In: PROCEEDINGS OF EASE 2019 - EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING, Association for Computing Machinery , 2019, p. 340-345Conference paper (Refereed)
    Abstract [en]

    Reviewing source code is a common practice in a modern and collaborative coding environment. In the past few years, the research on modern code reviews has gained interest among practitioners and researchers. The objective of our investigation is to observe the evolution of research related to modern code reviews, identify research gaps and serve as a basis for future research. We use a systematic mapping approach to identify and classify 177 research papers. As preliminary result of our investigation, we present in this paper a classification scheme of the main contributions of modern code review research between 2005 and 2018. © 2019 Association for Computing Machinery.

  • 5.
    Badampudi, Deepika
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Britto, Ricardo
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Modern Code Reviews - Survey of Literature and Practice2023In: ACM Transactions on Software Engineering and Methodology, ISSN 1049-331X, E-ISSN 1557-7392, Vol. 32, no 4, article id 107Article, review/survey (Refereed)
    Abstract [en]

    Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist, it is uanknown whether the research community has targeted themes that practitioners consider important.Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners' opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues.Method: We conducted a systematic mapping study to survey state of the art until and including 2021, employed the Q-Methodology to analyze the practitioners' perception of the relevance of MCR research, and analyzed the primary studies' research impact.Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1,300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor- and support systems-related research.Conclusion: These results indicate a misalignment between the state of the art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda that can potentially increase the impact of MCR research. © 2023 Copyright held by the owner/author(s).

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  • 6.
    Baldassarre, Maria Teresa
    et al.
    University of Bari, ITA.
    Caivano, Danilo
    University of Bari, ITA.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Romano, Simone
    University of Bari, ITA.
    Scanniello, Giuseppe
    University of Basilicata, ITA.
    Affective reactions and test-driven development: Results from three experiments and a survey2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 185, article id 111154Article in journal (Refereed)
    Abstract [en]

    The research on the claimed effects of Test-Driven Development (TDD) on software quality and developers’ productivity has shown inconclusive results. Some researchers have ascribed such results to the negative affective reactions that TDD would provoke when developers apply it. In this paper, we studied whether and in which phases TDD influences the affective states of developers, who are new to this development approach. To that end, we conducted a baseline experiment and two replications, and analyzed the data from these experiments both individually and jointly. Also, we performed methodological triangulation by means of an explanatory survey, whose respondents were experienced with TDD. The results of the baseline experiment suggested that developers like TDD significantly less, compared to a non-TDD approach. Also, developers who apply TDD like implementing production code significantly less than those who apply a non-TDD approach, while testing production code makes TDD developers significantly less happy. These results were not confirmed in the replicated experiments. We found that the moderator that better explains these differences across experiments is experience (in months) with unit testing, practiced in a test-last manner. The higher the experience with unit testing, the more negative the affective reactions caused by TDD. The results from the survey seem to confirm the role of this moderator. © 2021

  • 7.
    Bauer, Andreas
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards Collaborative GUI-based Testing2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Context:Contemporary software development is a socio-technical activity requiring extensive collaboration among individuals with diverse expertise.

    Software testing is an integral part of software development that also depends on various expertise.

    GUI-based testing allows assessing a system’s GUI and its behavior through its graphical user interface.

    Collaborative practices in software development, like code reviews, not only improve software quality but also promote knowledge exchange within teams.

    Similar benefits could be extended to other areas of software engineering, such as GUI-based testing.

    However, collaborative practices for GUI-based testing necessitate a unique approach since general software development practices, perceivably, can not be directly transferred to software testing.

    Goal:This thesis contributes towards a tool-supported approach enabling collaborative GUI-based testing.

    Our distinct goals are (1) to identify processes and guidelines to enable collaboration on GUI-based testing artifacts and (2) to operationalize tool support to aid this collaboration.

    Method:We conducted a systematic literature review identifying code review guidelines for GUI-based testing.

    Further, we conducted a controlled experiment to assess the efficiency and potential usability issues of Augmented Testing.

    Results:We provided guidelines for reviewing GUI-based testing artifacts, which aid contributors and reviewers during code reviews.

    We further provide empirical evidence that Augmented Testing is not only an efficient approach to GUI-based testing but also usable for non-technical users, making it a promising subject for further research in collaborative GUI-based testing.

    Conclusion:Code review guidelines aid collaboration through discussions, and a suitable testing approach can serve as a platform to operationalize collaboration.

    Collaborative GUI-based testing has the potential to improve the efficiency and effectiveness of such testing.

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  • 8.
    Bauer, Andreas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    We Tried and Failed: An Experience Report on a Collaborative Workflow for GUI-based Testing2023In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1-9Conference paper (Refereed)
    Abstract [en]

    Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact's quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did. © 2023 IEEE.

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  • 9.
    Bauer, Andreas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Coppola, Ricardo
    Politecnico di Torino, Italy.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Code review guidelines for GUI-based testing artifacts2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 163, article id 107299Article, review/survey (Refereed)
    Abstract [en]

    Context: Review of software artifacts, such as source or test code, is a common practice in industrial practice. However, although review guidelines are available for source and low-level test code, for GUI-based testing artifacts, such guidelines are missing. Objective: The goal of this work is to define a set of guidelines from literature about production and test code, that can be mapped to GUI-based testing artifacts. Method: A systematic literature review is conducted, using white and gray literature to identify guidelines for source and test code. These synthesized guidelines are then mapped, through examples, to create actionable, and applicable, guidelines for GUI-based testing artifacts. Results: The results of the study are 33 guidelines, summarized in nine guideline categories, that are successfully mapped as applicable to GUI-based testing artifacts. Of the collected literature, only 10 sources contained test-specific code review guidelines. These guideline categories are: perform automated checks, use checklists, provide context information, utilize metrics, ensure readability, visualize changes, reduce complexity, check conformity with the requirements and follow design principles and patterns. Conclusion: This pivotal set of guidelines provides an industrial contribution in filling the gap of general guidelines for review of GUI-based testing artifacts. Additionally, this work highlights, from an academic perspective, the need for future research in this area to also develop guidelines for other specific aspects of GUI-based testing practice, and to take into account other facets of the review process not covered by this work, such as reviewer selection. © 2023 The Author(s)

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  • 10.
    Bauer, Andreas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Augmented Testing to support Manual GUI-based Regression Testing: An Empirical StudyManuscript (preprint) (Other academic)
    Abstract [en]

    Context: Manual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information.

    Objective: The primary objective of this study is to gather empirical evidence regarding AT's efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT's benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout.

    Method: We conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study's quantitative results.

    Results: The results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient.When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues.

    Conclusion: This study makes an empirical contribution to understanding Augmented Testing, a promising approach to improve the efficiency of GUI-based regression testing in practice. Furthermore, it underscores the importance of continual refinements of AT.

  • 11.
    Chatzipetrou, Panagiota
    et al.
    Orebro Univ, SWE.
    Papatheocharous, Efi
    RISE Res Inst Sweden AB, SWE.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Inst Technol, Software Engn Res Lab SERL, Karlskrona, Sweden..
    Borg, Markus
    RISE Res Inst Sweden AB, SWE.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Inst Technol, Software Engn Res Lab SERL, Karlskrona, Sweden..
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Inst Technol, Software Engn Res Lab SERL, Karlskrona, Sweden..
    Component attributes and their importance in decisions and component selection2020In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 28, no 2, p. 567-593Article in journal (Refereed)
    Abstract [en]

    Component-based software engineering is a common approach in the development and evolution of contemporary software systems. Different component sourcing options are available, such as: (1) Software developed internally (in-house), (2) Software developed outsourced, (3) Commercial off-the-shelf software, and (4) Open-Source Software. However, there is little available research on what attributes of a component are the most important ones when selecting new components. The objective of this study is to investigate what matters the most to industry practitioners when they decide to select a component. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using compositional data analysis. The results of this exploratory analysis showed that cost was clearly considered to be the most important attribute for component selection. Other important attributes for the practitioners were: support of the component, longevity prediction, and level of off-the-shelf fit to product. Moreover, several practitioners still consider in-house software development to be the sole option when adding or replacing a component. On the other hand, there is a trend to complement it with other component sourcing options and, apart from cost, different attributes factor into their decision. Furthermore, in our analysis, nonparametric tests and biplots were used to further investigate the practitioners' inherent characteristics. It seems that smaller and larger organizations have different views on what attributes are the most important, and the most surprising finding is their contrasting views on the cost attribute: larger organizations with mature products are considerably more cost aware.

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    Component attributes and their importancein decisions and component selection
  • 12.
    Chatzipetrou, Panagiota
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Requirements' Characteristics: How do they Impact on Project Budget in a Systems Engineering Context?2019In: EUROMICRO Conference Proceedings / [ed] Staron, M; Capilla, R; Skavhaug, A, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 260-267Conference paper (Refereed)
    Abstract [en]

    Background: Requirements engineering is of a principal importance when starting a new project. However, the number of the requirements involved in a single project can reach up to thousands. Controlling and assuring the quality of natural language requirements (NLRs), in these quantities, is challenging. Aims: In a field study, we investigated with the Swedish Transportation Agency (STA) to what extent the characteristics of requirements had an influence on change requests and budget changes in the project. Method: We choose the following models to characterize system requirements formulated in natural language: Concern-based Model of Requirements (CMR), Requirements Abstractions Model (RAM) and Software-Hardware model (SHM). The classification of the NLRs was conducted by the three authors. The robust statistical measure Fleiss' Kappa was used to verify the reliability of the results. We used descriptive statistics, contingency tables, results from the Chi-Square test of association along with post hoc tests. Finally, a multivariate statistical technique, Correspondence analysis was used in order to provide a means of displaying a set of requirements in two-dimensional graphical form. Results: The results showed that software requirements are associated with less budget cost than hardware requirements. Moreover, software requirements tend to stay open for a longer period indicating that they are 'harder' to handle. Finally, the more discussion or interaction on a change request can lower the actual estimated change request cost. Conclusions: The results lead us to a need to further investigate the reasons why the software requirements are treated differently from the hardware requirements, interview the project managers, understand better the way those requirements are formulated and propose effective ways of Software management. © 2019 IEEE.

  • 13. Chuprina, Tatiana
    et al.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards Artefact-based Requirements Engineering for Data-Centric Systems2021In: CEUR Workshop Proceedings / [ed] Aydemir F.B.,Gralha C.,Daneva M.,Groen E.C.,Herrmann A.,Mennig P.,Abualhaija S.,Ferrari A.,Guo J.,Guizzardi R.,Horkoff J.,Perini A.,Susi A.,Breaux T.,Franch X.,Ernst N.,Paja E.,Seyff N., CEUR-WS , 2021, Vol. 2857Conference paper (Refereed)
    Abstract [en]

    Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at run-Time rendering such non-deterministic systems as complex. This complexity growth affects our understanding on needs and practices in Requirements Engineering (RE). There is, however, still little guidance on how to handle requirements for such systems effectively: What are, for example, typical quality requirements classes What modelling concepts do we rely on or which levels of abstraction do we need to consider In fact, how to integrate such concepts into approaches for a more traditional RE still needs profound investigations. In this research preview paper, we report on ongoing efforts to establish an artefact-based RE approach for the development of datacentric systems (DCSs). To this end, we sketch a DCS development process with the newly proposed requirements categories and data-centric artefacts and briefly report on an ongoing investigation of current RE challenges in industry developing data-centric systems. © 2021 CEUR-WS. All rights reserved.

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  • 14.
    Coppola, Riccardo
    et al.
    Politecnico di Torino, ITA.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A taxonomy of metrics for GUI-based testing research: A systematic literature review2022In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 152, article id 107062Article, review/survey (Refereed)
    Abstract [en]

    Context: GUI-based testing is a sub-field of software testing research that has emerged in the last three decades. GUI-based testing techniques focus on verifying the functional conformance of the system under test (SUT) through its graphical user interface. However, despite the research domains growth, studies in the field have low reproducibility and comparability. One observed cause of these phenomena is identified as a lack of research rigor and commonly used metrics, including coverage metrics. Objective: We aim to identify the most commonly used metrics in the field and formulate a taxonomy of coverage metrics for GUI-based testing research. Method: We adopt an evidence-based approach to build the taxonomy through a systematic literature review of studies in the GUI-based testing domain. Identified papers are then analyzed with Open and Axial Coding techniques to identify hierarchical and mutually exclusive categories of metrics with common characteristics, usages, and applications. Results: Through the analysis of 169 papers and 315 metric definitions, we obtained a taxonomy with 55 codes (common names for metrics), 17 metric categories, and 4 higher level categories: Functional Level, GUI Level, Model Level and Code Level. We measure a higher number of mentions of Model and Code level metrics over Functional and GUI level metrics. Conclusions: We propose a taxonomy for use in future GUI-based testing research to improve the general quality of studies in the domain. In addition, the taxonomy is perceived to help enable more replication studies as well as macro-analysis of the current body of research. © 2022 Elsevier B.V.

  • 15.
    Dehghani, Razieh
    et al.
    Sharif University of Technology, IRN.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ramsin, Raman
    Sharif University of Technology, IRN.
    On Understanding the Relation of Knowledge and Confidence to Requirements Quality2021In: REQUIREMENTS ENGINEERING: FOUNDATION FOR SOFTWARE QUALITY (REFSQ 2021) / [ed] Dalpiaz F., Spoletini P., Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12685, p. 208-224Conference paper (Refereed)
    Abstract [en]

    [Context and Motivation] Software requirements are affected by the knowledge and confidence of software engineers. Analyzing the interrelated impact of these factors is difficult because of the challenges of assessing knowledge and confidence. [Question/Problem] This research aims to draw attention to the need for considering the interrelated effects of confidence and knowledge on requirements quality, which has not been addressed by previous publications. [Principal ideas/results] For this purpose, the following steps have been taken: 1) requirements quality was defined based on the instructions provided by the ISO29148:2011 standard, 2) we selected the symptoms of low qualified requirements based on ISO29148:2011, 3) we analyzed five Software Requirements Specification (SRS) documents to find these symptoms, 3) people who have prepared the documents were categorized in four classes to specify the more/less knowledge and confidence they have regarding the symptoms, and 4) finally, the relation of lack of enough knowledge and confidence to symptoms of low quality was investigated. The results revealed that the simultaneous deficiency of confidence and knowledge has more negative effects in comparison with a deficiency of knowledge or confidence. [Contribution] In brief, this study has achieved these results: 1) the realization that a combined lack of knowledge and confidence has a larger effect on requirements quality than only one of the two factors, 2) the relation between low qualified requirements and requirements engineers’ needs for knowledge and confidence, and 3) variety of requirements engineers’ needs for knowledge based on their abilities to make discriminative and consistent decisions. © 2021, Springer Nature Switzerland AG.

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  • 16.
    Dorner, Michael
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Šmite, Darja
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Czerwonka, Jacek
    Microsoft Research, USA.
    Only Time Will Tell: Modelling Information Diffusion in Code Review with Time-Varying Hypergraphs2022In: ESEM '22: Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement / [ed] Madeiral F., Lassenius C., Lassenius C., Conte T., Mannisto T., Association for Computing Machinery (ACM), 2022, p. 195-204Conference paper (Refereed)
    Abstract [en]

    Background: Modern code review is expected to facilitate knowledge sharing: All relevant information, the collective expertise, and meta-information around the code change and its context become evident, transparent, and explicit in the corresponding code review discussion. The discussion participants can leverage this information in the following code reviews; the information diffuses through the communication network that emerges from code review. Traditional time-aggregated graphs fall short in rendering information diffusion as those models ignore the temporal order of the information exchange: Information can only be passed on if it is available in the first place.

    Aim: This manuscript presents a novel model based on time-varying hypergraphs for rendering information diffusion that overcomes the inherent limitations of traditional, time-aggregated graph-based models. 

    Method: In an in-silico experiment, we simulate an information diffusion within the internal code review at Microsoft and show the empirical impact of time on a key characteristic of information diffusion: the number of reachable participants. 

    Results: Time-aggregation significantly overestimates the paths of information diffusion available in communication networks and, thus, is neither precise nor accurate for modelling and measuring the spread of information within communication networks that emerge from code review. 

    Conclusion: Our model overcomes the inherent limitations of traditional, static or time-aggregated, graph-based communication models and sheds the first light on information diffusion through code review. We believe that our model can serve as a foundation for understanding, measuring, managing, and improving knowledge sharing in code review in particular and information diffusion in software engineering in general.

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  • 17.
    Fagerholm, Fabian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Becker, Christoph
    University of Toronto, CAN.
    Chatzigeorgiou, Alexander
    Panepistimion Makedonias, GRE.
    Betz, Stefanie
    Hochschule Furtwangen, DEU.
    Duboc, Leticia
    La Salle Univ., ESP.
    Penzenstadler, Birgit
    Lappeenrannan Teknillinen Yliopisto, FIN.
    Mohanani, Rahul
    Indraprastha Institute of Information Technology, IND.
    Venters, Colin C.
    University of Huddersfield, GBR.
    Temporal Discounting in Software Engineering: A Replication Study2019In: International Symposium on Empirical Software Engineering and Measurement, IEEE Computer Society, 2019Conference paper (Refereed)
    Abstract [en]

    Background: Many decisions made in Software Engineering practices are intertemporal choices: trade-offs in time between closer options with potential short-term benefit and future options with potential long-term benefit. However, how software professionals make intertemporal decisions is not well understood. Aim: This paper investigates how shifting time frames influence preferences in software projects in relation to purposefully selected background factors. Method: We investigate temporal discounting by replicating a questionnaire-based observational study. The replication uses a changed-population and -experimenter design to increase the internal and external validity of the original results. Results: The results of this study confirm the occurrence of temporal discounting in samples of both professional and student participants from different countries and demonstrate strong variance in discounting between study participants. We found that professional experience influenced discounting. Participants with broader professional experience exhibited less discounting than those with narrower experience. Conclusions: The results provide strong empirical support for the relevance and importance of temporal discounting in SE and the urgency of targeted interdisciplinary research to explore the underlying mechanisms and their theoretical and practical implications. The results suggest that technical debt management could be improved by increasing the breadth of experience available for critical decisions with long-term impact. In addition, the present study provides a methodological basis for replicating temporal discounting studies in software engineering. © 2019 IEEE.

  • 18.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Femmer, Henning
    Qualicen GmbH, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, DEU.
    What makes agile test artifacts useful?: An activity-based quality model from a practitioners' perspective2020In: International Symposium on Empirical Software Engineering and Measurement, IEEE Computer Society, 2020, article id 3421462Conference paper (Refereed)
    Abstract [en]

    Background: The artifacts used in Agile software testing and the reasons why these artifacts are used are fairly well-understood. However, empirical research on how Agile test artifacts are eventually designed in practice and which quality factors make them useful for software testing remains sparse. Aims: Our objective is two-fold. First, we identify current challenges in using test artifacts to understand why certain quality factors are considered good or bad. Second, we build an Activity-Based Artifact Quality Model that describes what Agile test artifacts should look like. Method: We conduct an industrial survey with 18 practitioners from 12 companies operating in seven different domains. Results: Our analysis reveals nine challenges and 16 factors describing the quality of six test artifacts from the perspective of Agile testers. Interestingly, we observed mostly challenges regarding language and traceability, which are well-known to occur in non-Agile projects. Conclusions: Although Agile software testing is becoming the norm, we still have little confidence about general do's and don'ts going beyond conventional wisdom. This study is the first to distill a list of quality factors deemed important to what can be considered as useful test artifacts. © 2020 IEEE Computer Society. All rights reserved.

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  • 19.
    Fischbach, Jannik
    et al.
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Spaans, Arjen
    Qualicen GmbH, DEU.
    Kummeth, Maximilian
    Qualicen GmbH, DEU.
    Vogelsang, Andreas
    University of Cologne, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Automatic Detection of Causality in Requirement Artifacts: The CiRA Approach2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Dalpiaz F., Spoletini P., Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12685, p. 19-36Conference paper (Refereed)
    Abstract [en]

    [Context & motivation:] System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance. [Question/problem:] We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not. Second, we need to understand and extract their causal relations. At present, though, we lack knowledge about the form and complexity of causality in requirements, which is necessary to develop a suitable approach addressing these two problems. [Principal ideas/results:] We conduct an exploratory case study with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA, standing for Causality in Requirement Artifacts). This constitutes a first step towards causality extraction from NL requirements. [Contribution:] We report on a case study and the resulting tool-supported approach for causality detection in requirements. Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal. We further demonstrate that our tool CiRA achieves a macro-F 1 score of 82% on real word data and that it outperforms related approaches with an average gain of 11.06% in macro-Recall and 11.43% in macro-Precision. Finally, we disclose our open data sets as well as our tool to foster the discourse on the automatic detection of causality in the RE community. © 2021, Springer Nature Switzerland AG.

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  • 20.
    Fischbach, Jannik
    et al.
    Netlight Consulting GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wehrle, Andreas
    Allianz Deutschland AG, DEU.
    Henao, Pablo Restrepo
    Netlight Consulting GmbH, DEU.
    Yousefi, Parisa
    Ericsson, SWE.
    Juricic, Tedi
    Ericsson, SWE.
    Radduenz, Jeannette
    Allianz Deutschland AG, DEU.
    Wiecher, Carsten
    Leopold Kostal GmbH & Co. KG, DEU.
    Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study2023In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 197, article id 111549Article in journal (Refereed)
    Abstract [en]

    Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8% can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/. © 2022

  • 21.
    Franch, Xavier
    et al.
    Universitat Politecnica de Catalunya, ESP.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    Technische Universitat Berlin, DEU.
    Heldal, Rogardt
    Western Norway University of Applied Sciences, NOR.
    Knauss, Eric
    Chalmers tekniska högskola, SWE.
    Oriol, Marc
    Universitat Politecnica de Catalunya, ESP.
    Travassos, Guilherme
    Federal University of Rio de Janeiro, BRA.
    Carver, Jeffrey C.
    University of Alabama, USA.
    Zimmermann, Thomas
    Microsoft Corporation, USA.
    How do Practitioners Perceive the Relevance of Requirements Engineering Research?2022In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 48, no 6, p. 1947-1964Article in journal (Refereed)
    Abstract [en]

    Context: The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception. Objective: This paper presents the results of a study aimed at gathering evidence from practitioners about their perception of the relevance of RE research and at understanding the factors that influence that perception. Method: We conducted a questionnaire-based survey of industry practitioners with expertise in RE. The participants rated the perceived relevance of 435 scientific papers presented at five top RE-related conferences. Results: The 153 participants provided a total of 2,164 ratings. The practitioners rated RE research as essential or worthwhile in a majority of cases. However, the percentage of non-positive ratings is still higher than we would like. Among the factors that affect the perception of relevance are the paper?s links to industry, the research method used, and respondents? roles. The reasons for positive perceptions were primarily related to the relevance of the problem and the soundness of the solution, while the causes for negative perceptions were more varied. The respondents also provided suggestions for future research, including topics researchers have studied for decades, like elicitation or requirement quality criteria. Conclusions: The study is valuable for both researchers and practitioners. Researchers can use the reasons respondents gave for positive and negative perceptions and the suggested research topics to help make their research more appealing to practitioners and thus more prone to industry adoption. Practitioners can benefit from the overall view of contemporary RE research by learning about research topics that they may not be familiar with, and compare their perception with those of their colleagues to self-assess their positioning towards more academic research. IEEE

  • 22.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    CEREC: Causality Extraction from Requirements Artifacts2020In: Proceedings - 7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 79-82, article id 9233006Conference paper (Refereed)
    Abstract [en]

    The cause-effect recognition (CEREC) system provides an API for causality extraction tailored to the requirements engineering context. The library is written in Java and is released under the MIT open source license. In this paper, the underlying algorithm is described, and a demonstration of the active learning component for causality extraction is outlined. The results are promising and strengthen the confidence in exploring automation approaches for model-based testing. © 2020 IEEE.

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  • 23.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fischbach, Jannik
    Netlight Consulting GmbH and Fortiss GmbH, Germany.
    Bauer, Andreas
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from Natural Language Requirements2023In: Proceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023 / [ed] Schneider K., Dalpiaz F., Horkoff J., Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 68-71Conference paper (Refereed)
    Abstract [en]

    Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering and testing. Conditional requirements (e.g., 'If A or B then C.') imply causal relationships which - when extracted - allow to generate these acceptance tests automatically. This paper presents a tool from the CiRA (Causality In Requirements Artifacts) initiative, which automatically processes conditional natural language requirements and generates a minimal set of test case descriptions achieving full coverage. We evaluate the tool on a publicly available data set of 61 requirements from the requirements specification of the German Corona-Warn-App. The tool infers the correct test variables in 84.5% and correct variable configurations in 92.3% of all cases, which corroborates the feasibility of our approach. © 2023 IEEE.

  • 24.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fischbach, Jannik
    Qualicen GmbH, GER.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Andreas
    University of Cologne, GER.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Causality in requirements artifacts: prevalence, detection, and impact2023In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, no 1, p. 49-74Article in journal (Refereed)
    Abstract [en]

    Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-F 1 score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond. © 2022, The Author(s).

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  • 25.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Spinola, Rodrigo
    Virginia Commonwealth University, Richmond, USA.
    Mandic, Vladimir
    University of Novi Sad, Serbia.
    Tausan, Nebojsa
    University of Novi Sad, Serbia.
    Ahmad, Ovais
    Karlstad University.
    Gonzalez-Huerta, Javier
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    An initial Theory to Understand and Manage Requirements Engineering Debt in Practice2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 159, article id 107201Article in journal (Refereed)
    Abstract [en]

    Context

    Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored.

    Objective

    We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED).

    Method

    We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory.

    Results

    We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results.

    Conclusions

    The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

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    IST22_RED
  • 26.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Junker, Maximilian
    Qualicen GmbH, DEU.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. fortiss GmbH, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. fortiss GmbH, DEU.
    Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts2020In: Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 561-572, article id 9286079Conference paper (Refereed)
    Abstract [en]

    Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly available requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation we analyzed 4457 natural language sentences from 18 requirements documents, 558 of which were causal (12.52%). The best evaluation of a requirements document provided an automatic extraction of 48.57% cause-effect graphs on average, which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but lacks exploration of being scaled up for practical use. Evaluating the applicability of the automatic causality extraction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts from requirements artifacts. © 2020 ACM.

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  • 27.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Lloyd, Montgomery
    Universität Hamburg, DEU.
    Jannik, Fischbach
    Netlight GmbH / fortiss GmbH, DEU.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Live Extensible Ontology of Quality Factors for Textual Requirements2022In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Knauss E., Mussbacher G., Arora C., Bano M., Schneider, IEEE, 2022, p. 274-280Conference paper (Refereed)
    Abstract [en]

    Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defects in requirements artifacts that potentially propagate to later stages in the development life cycle. However, as a research community, we still lack a holistic perspective on quality factors. This inhibits not only a comprehensive understanding of the existing body of knowledge but also the effective use and evolution of these factors. To this end, we propose an ontology of quality factors for textual requirements, which includes (1) a structure framing quality factors and related elements and (2) a central repository and web interface making these factors publicly accessible and usable. We contribute the first version of both by applying a rigorous ontology development method to 105 eligible primary studies and construct a first version of the repository and interface. We illustrate the usability of the ontology and invite fellow researchers to a joint community effort to complete and maintain this knowledge repository. We envision our ontology to reflect the community's harmonized perception of requirements quality factors, guide reporting of new quality factors, and provide central access to the current body of knowledge.

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  • 28.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Montgomery, Lloyd
    Universität Hamburg, DEU.
    Fischbach, Jannik
    Qualicen GmbH, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Requirements Quality Research: a harmonized Theory, Evaluation, and RoadmapManuscript (preprint) (Other academic)
    Abstract [en]

    High-quality requirements minimize the risk of propagating defects to later stages of the software development life-cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary we contribute(1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field.

  • 29.
    Frattini, Julian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Montgomery, Lloyd
    University of Hamburg, Germany.
    Fischbach, Jannik
    Netlight Consulting GmbH, Germany.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Requirements quality research: a harmonized theory, evaluation, and roadmap2023In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, no 4, p. 507-520Article in journal (Refereed)
    Abstract [en]

    High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap. © 2023, The Author(s).

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  • 30.
    Fucci, Davide
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Alégroth, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Axelsson, Thomas
    COMPANY, SWE.
    When traceability goes awry: An industrial experience report?2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 192, article id 111389Article in journal (Refereed)
    Abstract [en]

    The concept of traceability between artifacts is considered an enabler for software project success. This concept has received plenty of attention from the research community and is by many perceived to always be available in an industrial setting. In this industry-academia collaborative project, a team of researchers, supported by testing practitioners from a large telecommunication company, sought to investigate the partner company's issues related to software quality. However, it was soon identified that the fundamental traceability links between requirements and test cases were missing. This lack of traceability impeded the implementation of a solution to help the company deal with its quality issues. In this experience report, we discuss lessons learned about the practical value of creating and maintaining traceability links in complex industrial settings and provide a cautionary tale for researchers. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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  • 31.
    Garousi, Vahid
    et al.
    Queen's University Belfast, GBR.
    Bauer, Sara
    University of Innsbruck, AUT.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    NLP-assisted software testing: a systematic mapping of the literature2020In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 126, article id 106321Article, review/survey (Refereed)
    Abstract [en]

    Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners. © 2020 Elsevier B.V.

  • 32.
    Garousi, Vahid
    et al.
    Queens Univ Belfast, GBR.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Kuhrmann, Marco
    Tech Univ Clausthal, DEU.
    Herkiloglu, Kadir
    HAVELSAN AS, TUR.
    Eldh, Sigrid
    Ericsson AB, SWE.
    Exploring the industry's challenges in software testing: An empirical study2020In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 32, no 8, article id e2251Article in journal (Refereed)
    Abstract [en]

    Context Software testing is an important and costly software engineering activity in the industry. Despite the efforts of the software testing research community in the last several decades, various studies show that still many practitioners in the industry report challenges in their software testing tasks. Objective To shed light on industry's challenges in software testing, we characterize and synthesize the challenges reported by practitioners. Such concrete challenges can then be used for a variety of purposes, eg, research collaborations between industry and academia. Method Our empirical research method is opinion survey. By designing an online survey, we solicited practitioners' opinions about their challenges in different testing activities. Our dataset includes data from 72 practitioners from eight different countries. Results Our results show that test management and test automation are considered the most challenging among all testing activities by practitioners. Our results also include a set of 104 concrete challenges in software testing that may need further investigations by the research community. Conclusion We conclude that the focal points of industrial work and academic research in software testing differ. Furthermore, the paper at hand provides valuable insights concerning practitioners' "pain" points and, thus, provides researchers with a source of important research topics of high practical relevance.

  • 33.
    Garousi, Vahid
    et al.
    Wageningen University, NLD.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Institute of Technology.
    Nur Kılıçaslan, Feyza Nur
    Hacettepe Üniversitesi, TUR.
    A survey on software testability2019In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 108, p. 35-64Article in journal (Refereed)
    Abstract [en]

    Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers (published between 1982 and 2017). Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability.Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects. To assess potential benefits of this review paper, we shared its draft version with two of our industrial collaborators. They stated that they found the review useful and beneficial in their testing activities. Our results can also benefit researchers in observing the trends in this area and identify the topics that require further investigation.

  • 34.
    Garousi, Vahid
    et al.
    Wageningen University and Research Centre, NLD.
    Giray, Görkem
    Independent Researcher, TUR.
    Tüzün, Eray
    Bilkent Üniversitesi, TUR.
    Catal, Cagatay
    Wageningen University and Research Centre, NLD.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Aligning software engineering education with industrial needs: A meta-analysis2019In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 156, p. 65-83Article in journal (Refereed)
    Abstract [en]

    Context: According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Objective: Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. Method: To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Results: Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. Conclusion: This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry. © 2019 Elsevier Inc.

  • 35.
    Garousi, Vahid
    et al.
    Wageningen University and Research Centre, NLD.
    Pfahl, Dietmar
    University of Tartu, EST.
    Fernandes, Joao
    Universidade do Minho, PRT.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mäntylä, Mika
    University of Oulu, FIN.
    Shepherd, David
    ABB Group, USA.
    Arcuri, Andrea
    University of Luxembourg, LUX.
    Coşkunçay, Ahmet
    Ataturk University, TUR.
    Tekinerdogan, Bedir
    Wageningen University and Research Centre, NLD.
    Characterizing industry-academia collaborations in software engineering: evidence from 101 projects2019In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 24, no 4, p. 2540-2602Article in journal (Refereed)
    Abstract [en]

    Research collaboration between industry and academia supports improvement and innovation in industry and helps ensure the industrial relevance of academic research. However, many researchers and practitioners in the community believe that the level of joint industry-academia collaboration (IAC) projects in Software Engineering (SE) research is relatively low, creating a barrier between research and practice. The goal of the empirical study reported in this paper is to explore and characterize the state of IAC with respect to industrial needs, developed solutions, impacts of the projects and also a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review (SLR) study. To address the above goal, we conducted an opinion survey among researchers and practitioners with respect to their experience in IAC. Our dataset includes 101 data points from IAC projects conducted in 21 different countries. Our findings include: (1) the most popular topics of the IAC projects, in the dataset, are: software testing, quality, process, and project managements; (2) over 90% of IAC projects result in at least one publication; (3) almost 50% of IACs are initiated by industry, busting the myth that industry tends to avoid IACs; and (4) 61% of the IAC projects report having a positive impact on their industrial context, while 31% report no noticeable impacts or were “not sure”. To improve this situation, we present evidence-based recommendations to increase the success of IAC projects, such as the importance of testing pilot solutions before using them in industry. This study aims to contribute to the body of evidence in the area of IAC, and benefit researchers and practitioners. Using the data and evidence presented in this paper, they can conduct more successful IAC projects in SE by being aware of the challenges and how to overcome them, by applying best practices (patterns), and by preventing anti-patterns. © 2019, The Author(s).

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  • 36.
    Gasiba, Tiago Espinha
    et al.
    Siemens AG, DEU.
    Lechner, Ulrike
    Universität der Bundeswehr München, DEU.
    Pinto-Albuquerque, Maria
    Instituto Universitário de Lisboa (ISCTE-IUL), PRT.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Awareness of secure coding guidelines in the industry - A first data analysis2020In: Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020 / [ed] Wang G.,Ko R.,Bhuiyan M.Z.A.,Pan Y., Institute of Electrical and Electronics Engineers Inc. , 2020, p. 345-352Conference paper (Refereed)
    Abstract [en]

    Software needs to be secure, in particular, when deployed to critical infrastructures. Secure coding guidelines capture practices in industrial software engineering to ensure the security of code. This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines. The approach draws on well-established theories of policy compliance, neutralization theory, and security-related stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry. The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study. © 2020 IEEE.

  • 37.
    Ghafari, Mohammad
    et al.
    University of Bern, CHE.
    Gross, Timm
    University of Bern, CHE.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Felderer, Michael
    University of Innsbruck, AUT.
    Why research on test-driven development is inconclusive?2020In: International Symposium on Empirical Software Engineering and Measurement, IEEE Computer Society, 2020, article id 3410687Conference paper (Refereed)
    Abstract [en]

    [Background] Recent investigations into the effects of Test-Driven Development (TDD) have been contradictory and inconclusive. This hinders development teams to use research results as the basis for deciding whether and how to apply TDD. [Aim] To support researchers when designing a new study and to increase the applicability of TDD research in the decision-making process in industrial context, we aim at identifying the reasons behind the inconclusive research results in TDD. [Method] We studied the state of the art in TDD research published in top venues in the past decade, and analyzed the way these studies were set up. [Results] We identified five categories of factors that directly impact the outcome of studies on TDD. [Conclusions] This work can help researchers to conduct more reliable studies, and inform practitioners of risks they need to consider when consulting research on TDD. © 2020 IEEE Computer Society. All rights reserved.

  • 38.
    Girardi, Daniela
    et al.
    University of Bari, ITA.
    Novielli, Nicole
    University of Bari, ITA.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Lanubile, Filippo
    University of Bari, ITA.
    Recognizing developers' emotions while programming2020In: Proceedings - International Conference on Software Engineering, IEEE Computer Society, 2020, p. 666-677, article id 3380374Conference paper (Refereed)
    Abstract [en]

    Developers experience a wide range of emotions during programming tasks, which may have an impact on job performance. In this paper, we present an empirical study aimed at (i) investigating the link between emotion and progress, (ii) understanding the triggers for developers' emotions and the strategies to deal with negative ones, (iii) identifying the minimal set of non-invasive biometric sensors for emotion recognition during programming tasks. Results confirm previous findings about the relation between emotions and perceived productivity. Furthermore, we show that developers' emotions can be reliably recognized using only a wristband capturing the electrodermal activity and heart-related metrics. © 2020 Association for Computing Machinery.

  • 39.
    Gren, Lucas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Berntsson Svensson, Richard
    Chalmers University of Technology, SWE.
    Is it possible to disregard obsolete requirements? a family of experiments in software effort estimation2021In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, no 3, p. 459-480Article in journal (Refereed)
    Abstract [en]

    Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s).

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  • 40.
    Grossmann, Juergen
    et al.
    Fraunhofer Inst Open Commun Syst, DEU.
    Felderer, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Viehmann, Johannes
    Fraunhofer Inst Open Commun Syst, DEU.
    Schieferdecker, Ina
    Fraunhofer Inst Open Commun Syst, DEU.
    A Taxonomy to Assess and Tailor Risk-Based Testing in Recent Testing Standards2020In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, no 1, p. 40-49Article in journal (Refereed)
    Abstract [en]

    This article provides a taxonomy for risk-based testing that serves as a tool to define, tailor, or assess such approaches. In this setting, the taxonomy is used to systematically identify deviations between the requirements from public standards and the individual testing approaches.

  • 41.
    Hehn, Jennifer
    et al.
    University of St. Gallen, CHF.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Uebernickel, Falk
    Hasso Plattner Institute, DEU.
    Brenner, Walter
    Universität St. Gallen, CHF.
    Broy, Manfred
    Technical University of Munich, DEU.
    On Integrating Design Thinking for a Human-Centered Requirements Engineering2020In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, no 2, p. 25-31Article in journal (Refereed)
    Abstract [en]

    In this position paper, we elaborate on the possibilities and needs to integrate Design Thinking into Requirements Engineering. We draw from our research and project experiences to compare what is understood as Design Thinking and Requirements Engineering considering their involved artifacts. We suggest three approaches for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies and point at open challenges for research and practice. IEEE

  • 42.
    Henao, Pablo Restrepo
    et al.
    Technical University of Munich, DEU.
    Fischbach, Jannik
    Qualicen GmbH, DEU.
    Spies, Dominik
    Qualicen GmbH, DEU.
    Frattini, Julian
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Vogelsang, Anderas
    University of Cologne, DEU.
    Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews2021In: Proceedings of the IEEE International Conference on Requirements Engineering / [ed] Yue T., Mirakhorli M., IEEE Computer Society , 2021, p. 80-86Conference paper (Refereed)
    Abstract [en]

    Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods. © 2021 IEEE.

  • 43.
    Iqbal, Tahira
    et al.
    Fortiss GmbH, DEU.
    Seyff, Norbert
    Fachhochschule Nordwestschweiz FHNW, DEU.
    Mendez, Daniel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Generating requirements out of thin air: Towards automated feature identification for new apps2019In: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 193-199, article id 8933543Conference paper (Refereed)
    Abstract [en]

    App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor. © 2019 IEEE.

  • 44.
    Klotins, Eriks
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A collaborative method for identification and prioritization of data sources in MDREManuscript (preprint) (Other academic)
    Abstract [en]

    Requirements engineering (RE) literature acknowledges the importance of stakeholder identification early in the software engineering activities. However, literature overlooks the challenge of identifying and selecting the right stakeholders and the potential of using other inanimate requirements sources for RE activities for market-driven products.

    Market-driven products are influenced by a large number of stakeholders. Consulting all stakeholders directly is impractical, and companies utilize indirect data sources, e.g. documents and representatives of larger groups of stakeholders. However, without a systematic approach, companies often use easy to access or hard to ignore data sources for RE activities. As a consequence, companies waste resources on collecting irrelevant data or develop the product based on the input from a few sources, thus missing market opportunities.

    We propose a collaborative and structured method to support analysts in the identification and selection of the most relevant data sources for market-driven product engineering. The method consists of four steps and aims to build consensus between different perspectives in an organization and facilitates the identification of most relevant data sources. We demonstrate the use of the method with two industrial case studies.

    Our results show that the method can support market-driven requirements engineering in two ways: (1) by providing systematic steps to identify and prioritize data sources for RE, and (2) by highlighting and resolving discrepancies between different perspectives in an organization.

  • 45.
    Klotins, Eriks
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Sundelin, Katarina
    Ericsson AB Karlskrona, SWE.
    Falk, Erik
    Telia Co Uppsala, SWE.
    Towards cost-benefit evaluation for continuous software engineering activities2022In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 27, no 6, article id 157Article in journal (Refereed)
    Abstract [en]

    Context: Software companies must become better at delivering software to remain relevant in the market. Continuous integration and delivery practices promise to streamline software deliveries to end-users by implementing an automated software development and delivery pipeline. However, implementing or retrofitting an organization with such a pipeline is a substantial investment, while the reporting on benefits and their relevance in specific contexts/domains are vague. Aim: In this study, we explore continuous software engineering practices from an investment-benefit perspective. We identify what benefits can be attained by adopting continuous practices, what the associated investments and risks are, and analyze what parameters determine their relevance. Method: We perform a multiple case study to understand state-of-practice, organizational aims, and challenges in adopting continuous software engineering practices. We compare state-of-practice with state-of-the-art to validate the best practices and identify relevant gaps for further investigation. Results: We found that companies start the CI/CD adoption by automating and streamlining the internal development process with clear and immediate benefits. However, upgrading customers to continuous deliveries is a major obstacle due to existing agreements and customer push-back. Renegotiating existing agreements comes with a risk of losing customers and disrupting the whole organization. Conclusions: We conclude that the benefits of CI/CD are overstated in literature without considering the contextual and domain complexities rendering some benefits infeasible. We identify the need to understand the customer and organizational perspectives further and understand the contextual requirements towards the CI/CD.

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  • 46.
    Klotins, Eriks
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wilson, Magnus
    Ericsson AB, Sweden.
    Continuous Software Engineering: Introducing an Industry Readiness Model2023In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 40, no 4, p. 77-87Article in journal (Refereed)
    Abstract [en]

    Software is becoming essential for most products, manufacturing processes, and back-office functions. The speed of delivering new features and refining the product is critical to remaining competitive. Software organizations may adopt continuous engineering practices to become more efficient. However, retrofitting an organization with a pipeline is challenging. Importantly, the most significant challenges and opportunities, are related to, but stem from outside the engineering realm and require rethinking customer relationships and business models. This paper presents a hierarchy of continuous engineering benefits and challenges. It is aimed to guide the adoption of continuous practices in an organization to determine the current and target level of adoption, given organizational context, ambitions, and domain constraints. IEEE

  • 47.
    Klotins, Eriks
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Peretz-Andersson, Einav
    Jonkoping University, SWE.
    The unified perspective of digital transformation and continuous software engineering2022In: 5TH INTERNATIONAL WORKSHOP ON SOFTWARE-INTENSIVE BUSINESS: TOWARDS SUSTAINABLE SOFTWARE BUSINESS (IWSIB 2022), IEEE , 2022, p. 75-82Conference paper (Refereed)
    Abstract [en]

    Software is a key component of most products, services, industrial processes, and back-office functions. Thus, companies may gain an advantage by establishing fast feedback cycles to improve their software. Continuous software engineering (CI/CD) is being primarily studied as an engineering topic. However, the rest of the organization needs to align and be prepared to utilize the benefits of CI/CD. In this paper, we explore the overlap between CI/CD and digital transformation (DT). We study literature in both areas to develop a map of conditions, mechanisms, and outcomes. As a result, we present a unified perspective of CI/CD and DT. We found that CI/CD can be seen as an implementation of DT in a software organization. DT perspective can help to guide the adoption of CI/CD from an organizational perspective.

  • 48.
    Ljung, Kevin
    et al.
    Blekinge Institute of Technology. student.
    Gonzalez-Huerta, Javier
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    “To Clean Code or Not to Clean Code” A Survey Among Practitioners2022In: Product-Focused Software Process Improvement / [ed] Taibi D., Kuhrmann M., Mikkonen T.,, Springer Science+Business Media B.V., 2022, p. 298-315Conference paper (Refereed)
    Abstract [en]

    Context: Writing code that is understandable by other collaborators has become crucial to enhancing collaboration and productivity. Clean Code has become one of the most relevant software craftsmanship practices and has been widely embraced as a synonym for code quality by software developers and software development organizations all over the world. However, very little is known regarding whether developers agree with Clean Code principles and how they apply them in practice.

    Objectives: In this work, we investigated how developers perceive Clean Code principles, whether they believe that helps reading, understanding, reusing, and modifying Clean Code, and how they keep their code clean.

    Methods: We conducted a Systematic Literature Review in which we screened 771 research papers to collect Clean Code principles and a survey among 39 practitioners, some of them with more than 20 years of development experience.

    Results: So far, the results show a shared agreement with Clean Code principles and their potential benefits. They also show that developers tend to write “messy” code to be refactored later. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

  • 49.
    Minhas, Nasir Mehmood
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Börstler, Jürgen
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Regression testing for large-scale embedded software development: Exploring the state of practice2020In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 120, article id UNSP 106254Article in journal (Refereed)
    Abstract [en]

    Context: A majority of the regression testing techniques proposed by the research have not been adopted in industry. To increase adoption rates, we need to better understand the practitioners' perspectives on regression testing.

    Objective: This study aims at exploring the regression testing state of practice in the large-scale embedded software development. The study has two objectives, 1) to highlight the potential challenges in practice, and 2) to identify the industry-relevant research areas regarding regression testing.

    Method: We conducted a qualitative study in two large-scale embedded software development companies, where we carried out semi-structured interviews with representatives from five software testing teams. We did conduct the detailed review of the process documentation of the companies to complement/validate the findings of the interviews.

    Results: Mostly, the practitioners run regression testing with a selected scope, the selection of scope depends upon the size, complexity, and location of the change. Test cases are prioritized on the basis of risk and critical functionality. The practitioners rely on their knowledge and experience for the decision making regarding selection and prioritization of test cases.The companies are using both automated and manual regression testing, and mainly they rely on in-house developed tools for test automation. The challenges identified in the companies are: time to test, information management, test suite maintenance, lack of communication, test selection/prioritization, lack of assessment, etc. The proposed improvements are in line with the identified challenges. Regression testing goals identified in this study are customer satisfaction, critical defect detection, confidence, effectiveness, efficiency, and controlled slip through of faults.

    Conclusions: Considering the current state of practice and identified challenges we conclude that there is a need to reconsider the regression test strategy in the companies. Researchers need to analyze the industry perspective while proposing new regression testing techniques. The industry-academia collaboration projects would be a good platform in this regard.

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  • 50.
    Montgomery, Lloyd
    et al.
    University of Hamburg, Germany.
    Fucci, Davide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Bouraffa, Abir
    University of Hamburg, Germany.
    Scholz, Lisa
    University of Hamburg, Germany.
    Maalej, Walid
    University of Hamburg, Germany.
    Empirical research on requirements quality: a systematic mapping study2022In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 27, no 2, p. 183-209Article in journal (Refereed)
    Abstract [en]

    Research has repeatedly shown that high-quality requirements are essential for the success of development projects. While the term “quality” is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta-study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6905 articles from six academic databases, which we filtered down to 105 relevant primary studies. The primary studies use empirical research to explicitly define, improve, or evaluate requirements quality. We found that empirical research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as “template conformance” for consistency or “passive voice” for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as “requirements”, while little research targets quality attributes in specific types of requirements such as use cases or user stories. Our findings highlight the need to conduct more empirically grounded research defining requirements quality, using more varied research methods, and addressing a more diverse set of requirements types. © 2022, The Author(s).

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